Operational data is an important input used in estimating current damage level and remaining useful life of mechanical components. The overall useful life of certain items is dependent on the cumulative wear associated with individual subassemblies and/or components within the item.
Most industrial sensors capture data at low frequencies. However, many events occur over very small time periods, with wear accumulating in small increments over long operational times. Hence, it is important to sample signals at high frequency over a long period of time. High frequency sampling over long periods is seldom possible due to prohibitive costs.
Time series data analysis traditionally can rely on the available time series sampling frequency. Events occurring at frequencies higher than half of the sampling frequency are bound to be misrepresented by the sampled signal. In some instances, higher than normal sampling frequency data is available in order to record anomalies or specific stages of operation. Other methodologies that rely on frequency analysis are suitable only to signals with harmonic content. Some industrial application signals are random in nature or highly dependent on environmental effects.
For example, turbines (e.g., a turbine engine for aircraft, locomotive, power generation, etc.) can include variable stator vanes (VSV) that are angularly adjustable to control pressure ratios within the turbine compressor. A VSV wear prediction is highly correlated with accumulated change in angular position. Conventional collection of operational data is not necessarily collected at a frequency rate high enough to enable accurate estimation of VSV wear.